Functional principal component analysis as an alternative to mixed-effect models for describing sparse repeated measures in presence of missing data
arxiv(2024)
摘要
Analyzing longitudinal data in health studies is challenging due to sparse
and error-prone measurements, strong within-individual correlation, missing
data and various trajectory shapes. While mixed-effect models (MM) effectively
address these challenges, they remain parametric models and may incur
computational costs. In contrast, Functional Principal Component Analysis
(FPCA) is a non-parametric approach developed for regular and dense functional
data that flexibly describes temporal trajectories at a lower computational
cost. This paper presents an empirical simulation study evaluating the
behaviour of FPCA with sparse and error-prone repeated measures and its
robustness under different missing data schemes in comparison with MM. The
results show that FPCA is well-suited in the presence of missing at random data
caused by dropout, except in scenarios involving most frequent and systematic
dropout. Like MM, FPCA fails under missing not at random mechanism. The FPCA
was applied to describe the trajectories of four cognitive functions before
clinical dementia and contrast them with those of matched controls in a
case-control study nested in a population-based aging cohort. The average
cognitive declines of future dementia cases showed a sudden divergence from
those of their matched controls with a sharp acceleration 5 to 2.5 years prior
to diagnosis.
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